We introduce a new algorithm for binary classification in the selective sampling protocol. Our algorithm uses Regularized Least Squares (RLS) as base classifier, and for this reas...
Selectivity estimation of queries is an important and wellstudied problem in relational database systems. In this paper, we examine selectivity estimation in the context of Geogra...
In a variety of applications ranging from optimizing queries on alphanumeric attributes to providing approximate counts of documents containing several query terms, there is an in...
Zhiyuan Chen, Flip Korn, Nick Koudas, S. Muthukris...
Many state-of-the-art selectivity estimation methods use query feedback to maintain histogram buckets, thereby using the limited memory efficiently. However, they are "reacti...
Off-policy reinforcement learning is aimed at efficiently reusing data samples gathered in the past, which is an essential problem for physically grounded AI as experiments are us...